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New PDF release: Accelerating MATLAB with GPU Computing: A Primer with

By Jung W. Suh, Youngmin Kim

ISBN-10: 0124080804

ISBN-13: 9780124080805

Past simulation and set of rules improvement, many builders more and more use MATLAB even for product deployment in computationally heavy fields. This frequently calls for that MATLAB codes run speedier by way of leveraging the dispensed parallelism of photos Processing devices (GPUs). whereas MATLAB effectively presents high-level capabilities as a simulation instrument for fast prototyping, the underlying information and information wanted for using GPUs make MATLAB clients hesitate to step into it. Accelerating MATLAB with GPUs bargains a primer on bridging this gap.

Starting with the fundamentals, developing MATLAB for CUDA (in home windows, Linux and Mac OS X) and profiling, it then courses clients via complicated themes akin to CUDA libraries. The authors percentage their adventure constructing algorithms utilizing MATLAB, C++ and GPUs for enormous datasets, editing MATLAB codes to raised make the most of the computational strength of GPUs, and integrating them into advertisement software program items. during the booklet, they display many instance codes that may be used as templates of C-MEX and CUDA codes for readers' initiatives. obtain instance codes from the publisher's site: http://booksite.elsevier.com/9780124080805/

• indicates the best way to speed up MATLAB codes throughout the GPU for parallel processing, with minimum knowledge
• Explains the comparable heritage on undefined, structure and programming for ease of use
• presents uncomplicated labored examples of MATLAB and CUDA C codes in addition to templates that may be reused in real-world initiatives

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N} where item i has profit wi , and we need to select a maximum profit subset of items satisfying given constraints. A solution to these problems can be defined as a binary vector z = (z1 , . . , zn ) ∈ {0, 1}n, where zi = 1 iff item i (task Ti in our case) is selected. We remark that each choice of the dynamic program DP from the previous lemma corresponds to selecting one or more items, and the structure of DP guarantees that no item is selected more than once. Let Ci denote the choices of DP that select item i.

N} where item i has profit wi , and we need to select a maximum profit subset of items satisfying given constraints. A solution to these problems can be defined as a binary vector z = (z1 , . . , zn ) ∈ {0, 1}n, where zi = 1 iff item i (task Ti in our case) is selected. We remark that each choice of the dynamic program DP from the previous lemma corresponds to selecting one or more items, and the structure of DP guarantees that no item is selected more than once. Let Ci denote the choices of DP that select item i.

For the general (weighted) case, we present an extended formulation with integrality gap bounded by 7 + ε. , FOCS 2011]. This result exploits crucially a technique for embedding dynamic programs into linear programs. We believe that this method could be useful to strengthen LP-formulations for other problems as well and might eventually speed up computations due to stronger problem formulations. 1 Introduction In the Unsplittable Flow Problem on a Path (UFPP) we are given a set of n tasks T and a path G = (V, E) on m edges.

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Accelerating MATLAB with GPU Computing: A Primer with Examples by Jung W. Suh, Youngmin Kim


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